def create_learner(self):
alpha = self.alphas[self.alpha_index]
preprocessors = self.preprocessors
args = {"preprocessors": preprocessors}
if self.reg_type == OWLinearRegression.OLS:
learner = LinearRegressionLearner(**args)
elif self.reg_type == OWLinearRegression.Ridge:
learner = RidgeRegressionLearner(alpha=alpha, **args)
elif self.reg_type == OWLinearRegression.Lasso:
learner = LassoRegressionLearner(alpha=alpha, **args)
elif self.reg_type == OWLinearRegression.Elastic:
learner = ElasticNetLearner(alpha=alpha,
l1_ratio=1 - self.l2_ratio, **args)
return learner
def commit(self):
alpha = self.alphas[self.alpha_index]
preprocessors = self.preprocessors
if self.data is not None and np.isnan(self.data.Y).any():
self.warning(0, "Missing values of target variable(s)")
if not self.preprocessors:
if self.reg_type == OWLinearRegression.OLS:
preprocessors = LinearRegressionLearner.preprocessors
elif self.reg_type == OWLinearRegression.Ridge:
preprocessors = RidgeRegressionLearner.preprocessors
else:
preprocessors = LassoRegressionLearner.preprocessors
else:
preprocessors = list(self.preprocessors)
preprocessors.append(RemoveNaNClasses())
args = {"preprocessors": preprocessors}
if self.reg_type == OWLinearRegression.OLS:
learner = LinearRegressionLearner(**args)
elif self.reg_type == OWLinearRegression.Ridge:
learner = RidgeRegressionLearner(alpha=alpha, **args)
elif self.reg_type == OWLinearRegression.Lasso:
learner = LassoRegressionLearner(alpha=alpha, **args)
learner.name = self.learner_name
predictor = None
coef_table = None
self.error(0)
if self.data is not None:
if not learner.check_learner_adequacy(self.data.domain):
self.error(0, learner.learner_adequacy_err_msg)
else:
predictor = learner(self.data)
predictor.name = self.learner_name
domain = Domain(
[ContinuousVariable("coef", number_of_decimals=7)],
metas=[StringVariable("name")])
coefs = [predictor.intercept] + list(predictor.coefficients)
names = ["intercept"] + \
[attr.name for attr in predictor.domain.attributes]
coef_table = Table(domain, list(zip(coefs, names)))
coef_table.name = "coefficients"
self.send("Linear Regression", learner)
self.send("Model", predictor)
self.send("Coefficients", coef_table)
def commit(self):
alpha = self.alphas[self.alpha_index]
preprocessors = self.preprocessors
args = {"preprocessors": preprocessors}
if self.reg_type == OWLinearRegression.OLS:
learner = LinearRegressionLearner(**args)
elif self.reg_type == OWLinearRegression.Ridge:
learner = RidgeRegressionLearner(alpha=alpha, **args)
elif self.reg_type == OWLinearRegression.Lasso:
learner = LassoRegressionLearner(alpha=alpha, **args)
elif self.reg_type == OWLinearRegression.Elastic:
learner = ElasticNetLearner(alpha=alpha,
l1_ratio=self.l1_ratio,
**args)
learner.name = self.learner_name
predictor = None
coef_table = None
self.error(0)
if self.data is not None:
if not learner.check_learner_adequacy(self.data.domain):
self.error(0, learner.learner_adequacy_err_msg)
else:
predictor = learner(self.data)
predictor.name = self.learner_name
domain = Domain(
[ContinuousVariable("coef", number_of_decimals=7)],
metas=[StringVariable("name")])
coefs = [predictor.intercept] + list(predictor.coefficients)
names = ["intercept"] + \
[attr.name for attr in predictor.domain.attributes]
coef_table = Table(domain, list(zip(coefs, names)))
coef_table.name = "coefficients"
self.send("Linear Regression", learner)
self.send("Model", predictor)
self.send("Coefficients", coef_table)
def commit(self):
alpha = self.alphas[self.alpha_index]
args = {"preprocessors": self.preprocessors}
if self.reg_type == OWLinearRegression.OLS:
learner = LinearRegressionLearner(**args)
elif self.reg_type == OWLinearRegression.Ridge:
learner = RidgeRegressionLearner(alpha=alpha, **args)
elif self.reg_type == OWLinearRegression.Lasso:
learner = LassoRegressionLearner(alpha=alpha, **args)
learner.name = self.learner_name
predictor = None
self.error(0)
if self.data is not None:
if not learner.check_learner_adequacy(self.data.domain):
self.error(0, learner.learner_adequacy_err_msg)
else:
predictor = learner(self.data)
predictor.name = self.learner_name
self.send("Learner", learner)
self.send("Predictor", predictor)
if hasattr(model, "model"):
model = model.model
elif hasattr(model, "skl_model"):
model = model.skl_model
if model is not None and hasattr(model, "coef_"):
domain = Domain([ContinuousVariable("coef", number_of_decimals=7)],
metas=[StringVariable("name")])
coefs = [model.intercept_ + model.coef_[0]] + list(model.coef_[1:])
names = ["1", x_label] + \
["{}^{}".format(x_label, i) for i in range(2, degree + 1)]
coef_table = Table(domain, list(zip(coefs, names)))
self.send("Coefficients", coef_table)
else:
self.send("Coefficients", None)
if __name__ == "__main__":
import sys
from AnyQt.QtWidgets import QApplication
a = QApplication(sys.argv)
ow = OWUnivariateRegression()
learner = RidgeRegressionLearner(alpha=1.0)
polylearner = PolynomialLearner(learner, degree=2)
d = Table('iris')
ow.set_data(d)
ow.set_learner(learner)
ow.show()
a.exec_()
ow.saveSettings()
